Abstract
An accurate analysis of spatial rainfall distribution is of great importance for managing watershed water resources, in addition to giving support to meteorological studies and agricultural planning. This work compares the performance of two interpolation methods: Inverse distance weighted (IDW) and Kriging, in the analysis of annual rainfall spatial distribution. We use annual rainfall data for the state of Rio Grande do Sul (Brazil) from 1961 to 2017. To determine which proportion of the sample results in more accurate rainfall distribution maps, we use a certain amount of points close to the estimated point. We use mean squared error (MSE), coefficient of determination (R2), root mean squared error (RMSE) and modified Willmott's concordance index (md). We conduct random fields simulations study, and the performance of the geostatistics and classic methods for the exposed case was evaluated in terms of precision and accuracy obtained by Monte Carlo simulation to support the results. The results indicate that the co-ordinary Kriging interpolator showed better goodness of fit, assuming altitude as a covariate. We concluded that the geostatistical method of Kriging using nine closer points (50% of nearest neighbors) was the one that better represented annual rainfall spatial distribution in the state of Rio Grande do Sul.
Highlights
Rainfall is a measure of an ecosystem’s water availability and has strong relationships with the productivity of a region [1, 2]
The analysis of climatic variables essentially consists of two stages: an exploratory one, using descriptive statistics in order to verify the normality of the data and to discard the need for transformation in the set and to identify the existence of possible outliers; and
The occurrence of technical problems at the stations has meant that many other stations in the state have a much lower number of records than our work, which is similar in quantity to the work of Alvares et al [30]. Given these considerations and given the focus of the work, we analyze annual rainfall data. We emphasize that this approach constitutes one possibility of analysis, and others such as monthly, seasonal and/or cumulative annual analysis could be considered [11, 32, 45, 46]
Summary
Rainfall is a measure (indicator) of an ecosystem’s water availability and has strong relationships with the productivity of a region [1, 2]. Variables related to precipitation such as average, maximum and annual variability, among others, are important for explaining spatial patterns of anomalies, as well as allowing assessments of climate change by increasing the frequency of extreme events [2], Al‐Yaari et al.[3]). Its monitoring makes it possible to understand the hydrological cycle that influences ecological and environmental dynamics, affecting economic and social activities (Morales and Araujo [4]). It is essential to apply a spatial interpolation process where points with known values are used to estimate unknown values at other points
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